1. Field of the Invention
The present general inventive concept relates to an image processing method, and more particularly to a method of editing static digital combined images comprising a plurality of object images.
2. Description of the Related Art
Multi-Function Devices (MFC) are becoming increasingly popular recently. These devices are similar to a scanner or printer, and are equipped with an integrated system which can process various functions and problems. For example, an MFD may provide a function such as cropping or alignment of automatically removing an object image from a scanned image. Although it is difficult to make improvements regarding this function, there is a need for a function which automatically processes a combined image which includes a plurality of object images.
Conventional art includes various devices and methods aimed at resolving problems related to automatic processing of combined images. Especially, “Segmentation of Rectangular Objects Lying on an Unknown Background in a Small Preview Scan Image” [1] in a thesis written by Michael Guerzhoy and Hui Zhou at a Canadian conference on computer and robot vision in 2008 (CRV 2009) is one of those methods. This thesis [1] discloses a method of segmenting a rectangular object lying on a background having a texture which has been processed lightly with a color that is unknown in advance. This method is based on a set of heuristic methods of detecting edges of a rectangular object and a rough estimation on a background color in a process of generating a preliminary assumption on the existence of a rectangular object on an image selected after the most plausible assumption. Authors of the thesis [1] argue that the explained solution can discover objects which are repeated and adjacent to one another such as photos, bills or plastic cards in small size preview images. Nevertheless, an application program of the aforementioned solution is limited by an assumption of a significant color differentiation between the object and background.
“Recursive method to detect and segment multiple rectangular objects in scanned images” [2], a thesis announced by C. Herley at the 2003 IEEE symposium, for example, is based on identifying and segmenting rectangular objects which may include various distortions such as rough edges and smooth corners. The author of the thesis [2] believed that it was possible to obtain an effective and stable tool for segmentation by establishing a one dimensional projection. In addition, the author of the thesis [2] started from an assumption that the purpose of operation is in segmenting the objects from other objects based on the one dimensional project while each object display a consistent area. In fact, as in most of the cases, an object has a very little difference from the background and use of the explained resolving method may be recognized as a segmentation group divided spacially. The method explained in U.S. Pat. No. 7,483,589 [3] provides automatic framing of the document according to location changes and alignments of various documents existing in images. Herein, an application program where it is possible to copy a copy check bill which seems to be leveled and ordered is mentioned. In addition, for an automatic segmentation of object images, a critical processing of the initial image and an application program of morphological operation is used together. Therefore, the result of segmentation depends on the connectivity of each object. When the connectivity of each object is broken, it may lead to distortion of the segmentation result.
The method explained in U.S. Pat. No. 7,542,608 [4] provides an automatic framing of objects on images, and includes detecting planimetric lines of the objects, segmenting the images, and combining the images. Herein, in order to segment the images into background area and a plurality of foreground areas, an algorithm which searches for coherent constituent elements is used. Furthermore, in order to combine the foreground areas, distances between the foreground areas are calculated, in which case if a distance is smaller than a predetermined value, the areas are combined. In addition, this method combines smaller areas with bigger areas based on an estimation of the size of outline, while checking convenience of such a combination. If a width and length of an object exceed a predetermined value, the combination is deemed to be inappropriate. Assuming that this size is segmentation of the approximately known object, this condition is limited by the aforementioned application program.
As aforementioned, there are numerous problems related to developing an automatic method regarding segmentation of a plurality of object images displayed on one image. One of the most complex problem lies in detection of a light or white object image which may be segmented in parts during the processing procedure. The second most complex problem lies in separation of object images located closely to one another.